def _prepare_features_for_sqss(features, labels, mode,
sequence_feature_columns,
context_feature_columns):
"""Prepares features for batching by the SQSS.
In preparation for batching by the SQSS, this function:
- Extracts the input key from the features dict.
- Separates sequence and context features dicts from the features dict.
- Adds the labels tensor to the sequence features dict.
Args:
features: A dict of Python string to an iterable of `Tensor` or
`SparseTensor` of rank 2, the `features` argument of a TF.Learn model_fn.
labels: An iterable of `Tensor`.
mode: Defines whether this is training, evaluation or prediction.
See `ModeKeys`.
sequence_feature_columns: An iterable containing all the feature columns
describing sequence features. All items in the set should be instances
of classes derived from `FeatureColumn`.
context_feature_columns: An iterable containing all the feature columns
describing context features, i.e., features that apply accross all time
steps. All items in the set should be instances of classes derived from
`FeatureColumn`.
Returns:
sequence_features: A dict mapping feature names to sequence features.
context_features: A dict mapping feature names to context features.
Raises:
ValueError: If `features` does not contain a value for every key in
`sequence_feature_columns` or `context_feature_columns`.
"""
# Extract sequence features.
feature_column_ops._check_supported_sequence_columns(sequence_feature_columns) # pylint: disable=protected-access
sequence_features = {}
for column in sequence_feature_columns:
for name in _get_name_or_parent_names(column):
feature = features.get(name, None)
if feature is None:
raise ValueError('No key in features for sequence feature: ' + name)
sequence_features[name] = feature
# Extract context features.
context_features = {}
if context_feature_columns is not None:
for column in context_feature_columns:
name = column.name
feature = features.get(name, None)
if feature is None:
raise ValueError('No key in features for context feature: ' + name)
context_features[name] = feature
# Add labels to the resulting sequence features dict.
if mode != model_fn.ModeKeys.INFER:
sequence_features[rnn_common.RNNKeys.LABELS_KEY] = labels
return sequence_features, context_features
def _prepare_features_for_sqss(features, labels, mode, input_key_column_name,
sequence_feature_columns,
context_feature_columns):
"""Prepares features for batching by the SQSS.
In preparation for batching by the SQSS, this function:
- Extracts the input key from the features dict.
- Separates sequence and context features dicts from the features dict.
- Adds the labels tensor to the sequence features dict.
Args:
features: A dict of Python string to an iterable of `Tensor` or
`SparseTensor` of rank 2, the `features` argument of a TF.Learn model_fn.
labels: An iterable of `Tensor`.
mode: Defines whether this is training, evaluation or prediction.
See `ModeKeys`.
input_key_column_name: Python string, the name of the feature column
containing a string scalar `Tensor` that serves as a unique key to
identify the input sequence across minibatches.
sequence_feature_columns: An iterable containing all the feature columns
describing sequence features. All items in the set should be instances
of classes derived from `FeatureColumn`.
context_feature_columns: An iterable containing all the feature columns
describing context features, i.e., features that apply accross all time
steps. All items in the set should be instances of classes derived from
`FeatureColumn`.
Returns:
input_key: The string scalar `Tensor` that serves as a unique key to
identify the input sequence across minibatches.
sequence_features: A dict mapping feature names to sequence features.
context_features: A dict mapping feature names to context features.
Raises:
ValueError: If `features` does not contain a value for
`input_key_column_name`.
ValueError: If `features` does not contain a value for every key in
`sequence_feature_columns` or `context_feature_columns`.
"""
# Pop the input key from the features dict.
input_key = features.pop(input_key_column_name, None)
if input_key is None:
raise ValueError('No key in features for input_key_column_name: ' +
input_key_column_name)
# Extract sequence features.
feature_column_ops._check_supported_sequence_columns(
sequence_feature_columns) # pylint: disable=protected-access
sequence_features = {}
for column in sequence_feature_columns:
for name in _get_name_or_parent_names(column):
feature = features.get(name, None)
if feature is None:
raise ValueError('No key in features for sequence feature: ' +
name)
sequence_features[name] = feature
# Extract context features.
context_features = {}
if context_feature_columns is not None:
for column in context_feature_columns:
name = column.name
feature = features.get(name, None)
if feature is None:
raise ValueError('No key in features for context feature: ' +
name)
context_features[name] = feature
# Add labels to the resulting sequence features dict.
if mode != model_fn.ModeKeys.INFER:
sequence_features[RNNKeys.LABELS_KEY] = labels
return input_key, sequence_features, context_features
